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Volumn , Issue , 2008, Pages 143-150

A pruned problem transformation method for multi-label classification

Author keywords

Multi label classification; Problem transformation

Indexed keywords

MULTI-LABEL CLASSIFICATIONS; PROBLEM TRANSFORMATIONS; TRAINING TIME; YOUTUBE;

EID: 84880106608     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (215)

References (16)
  • 9
    • 0033886806 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using em
    • Kamal Nigam, Andrew K. Mccallum, Sebastian Thrun, and Tom M. Mitchell. Text classification from labeled and unlabeled documents using em. Machine Learning, 39 (2/3): 103-134, 2000.
    • (2000) Machine Learning , vol.39 , Issue.2-3 , pp. 103-134
    • Nigam, K.1    Mccallum, A.K.2    Thrun, S.3    Mitchell, T.M.4
  • 10
    • 0033905095 scopus 로고    scopus 로고
    • Boostexter: A boosting-based system for text categorization
    • Robert E. Schapire and Yoram Singer. Boostexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135-168, 2000.
    • (2000) Machine Learning , vol.39 , Issue.2-3 , pp. 135-168
    • Schapire, R.E.1    Singer, Y.2
  • 16
    • 84880128076 scopus 로고    scopus 로고
    • We have included 66 studies for the synthesis of evidence, of which 10 studies were found relevant to answer the first research question. The findings showed that personality type is the most common factor investigated. However, the results of those studies were somewhat inconclusive
    • We have included 66 studies for the synthesis of evidence, of which 10 studies were found relevant to answer the first research question. The findings showed that personality type is the most common factor investigated. However, the results of those studies were somewhat inconclusive.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.